Driver's Facial Expression Recognition Using Global Context Vision Transformer

Driver's facial expression recognition plays a critical role in enhancing driver safety, comfort, and overall driving experience by proactively mitigating potential road risks. While most existing works in this domain rely on CNN - based approaches, this paper proposes a novel method for driver facial expression recognition using Global Context Vision Transformer (DFER-GCViT). With its inherent capabilities of transformer-based architectures and global context modeling, the proposed method handles challenges commonly encountered in real-world driving scenarios, including occlusions, head pose variations, and illumination conditions. Our method consists of three modules: preprocessing for face detection and data augmentation, facial feature extraction of local and global features, and expression classification using a modified GC-ViT classifier. To evaluate the performance of DFER-GCViT, extensive experiments are conducted on two benchmarking datasets namely the KMU-FED driver facial expression dataset and FER2013 general facial expression dataset. The experimental results demonstrate the superiority of DFER-GCViT in accurately recognizing driver's facial expressions, achieving an average accuracy of 98.27 % on the KMU-FED dataset and 73.78% on the FER2013 dataset, outperforming several state-of-the-art methods on these two benchmarking datasets.